Harju Inka, Lange Christoph, Kostrzewa Markus, Maier Thomas, Rantakokko-Jalava Kaisu, Haanperä Marjo
Clinical Microbiology Laboratory, Turku University Hospital, Turku, Finland
Bruker Daltonik GmbH, Bremen, Germany.
J Clin Microbiol. 2017 Mar;55(3):914-922. doi: 10.1128/JCM.01990-16. Epub 2017 Jan 4.
Reliable distinction of and viridans group streptococci is important because of the different pathogenic properties of these organisms. Differentiation between and closely related species group streptococci has always been challenging, even when using such modern methods as 16S rRNA gene sequencing or matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. In this study, a novel algorithm combined with an enhanced database was evaluated for differentiation between and species group streptococci. One hundred one clinical species group streptococcal strains and 188 clinical strains were identified by both the standard MALDI Biotyper database alone and that combined with a novel algorithm. The database update from 4,613 strains to 5,627 strains drastically improved the differentiation of and species group streptococci: when the new database version containing 5,627 strains was used, only one of the 101 species group isolates was misidentified as , whereas 66 of them were misidentified as when the earlier 4,613-strain MALDI Biotyper database version was used. The updated MALDI Biotyper database combined with the novel algorithm showed even better performance, producing no misidentifications of the species group strains as All strains were correctly identified as with both the standard MALDI Biotyper database and the standard MALDI Biotyper database combined with the novel algorithm. This new algorithm thus enables reliable differentiation between pneumococci and other species group streptococci with the MALDI Biotyper.
由于这些微生物具有不同的致病特性,可靠区分肺炎链球菌和草绿色链球菌群很重要。区分肺炎链球菌和密切相关的草绿色链球菌群一直具有挑战性,即使使用16S rRNA基因测序或基质辅助激光解吸电离飞行时间(MALDI-TOF)质谱等现代方法也是如此。在本研究中,评估了一种结合增强数据库的新算法用于区分肺炎链球菌和草绿色链球菌群。通过单独使用标准MALDI Biotyper数据库以及将其与新算法相结合,对101株临床草绿色链球菌群菌株和188株临床肺炎链球菌菌株进行了鉴定。数据库从4613株更新到5627株极大地改善了肺炎链球菌和草绿色链球菌群的区分:当使用包含5627株的新数据库版本时,101株草绿色链球菌群分离株中只有1株被误鉴定为肺炎链球菌,而使用早期4613株的MALDI Biotyper数据库版本时,其中66株被误鉴定为肺炎链球菌。更新后的MALDI Biotyper数据库与新算法相结合表现出更好的性能,没有将草绿色链球菌群菌株误鉴定为肺炎链球菌。所有肺炎链球菌菌株使用标准MALDI Biotyper数据库以及与新算法相结合的标准MALDI Biotyper数据库均被正确鉴定为肺炎链球菌。因此,这种新算法能够通过MALDI Biotyper可靠地区分肺炎球菌和其他草绿色链球菌群。